The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. #Krita gaussian noise reduction full#This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. The experiment confirms that our system can detect and recognize different types of drinks with an accuracy of 98.51%. To perform experimentation a dataset is built containing ten most consumed soft drinks in Bangladesh using images from the ImageNet dataset, internet sources and also self-capturing. Finally, the size of each drink bottle is estimated using the bag-of-feature (BoF) and distance ratio calculation to find the nutrition value from the nutrition fact table. After removing backgrounds and segment out only the region of interest from the image a deep CNN-based transfer learning model is employed for the drink classification. Then the location of the drinks region is extracted through visual saliency and mean-shift segmentation technique. At first, a pre-processing function is done through noise reduction and contrast enhancement. To develop consciousness among people, the present work describes an image-based tool to self-monitor the nutritional information of soft drinks by using a deep convolutional neural network (CNN) along with transfer learning. Health experts around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases, and so on. Nowadays, people are taking soft drinks (carbonated nonalcoholic beverages) at an increasing rate. #Krita gaussian noise reduction free#Moreover, the proposed NR-IQA metric is beneficial in wood industry as a distortion free reference image is not needed to evaluate the wood images. Results shows that the proposed NR-IQA metric outperforms the BRISQUE and the FR-IQA metrics. The proposed IQA metric is compared with a widely used NR-IQA metric, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full Reference-IQA (FR-IQA) metrics. The proposed metric is developed by training the support vector machine regression with GLCM and Gabor features calculated for wood images together with scores obtained from subjective evaluation. Therefore, a Gray Level Co-Occurrence Matrix (GLCM) and Gabor features-based NR-IQA (GGNR-IQA) metric is proposed to assess the quality of wood images. To the best of our knowledge, there is no NR-IQA developed for wood images specifically. There is a need to develop a No-Reference IQA (NR-IQA) system as a perfect and distortion free wood images may be impossible to be acquired in the dusty environment in timber factories. Image Quality Assessment (IQA) is a vital element in improving the efficiency of an automatic recognition system of various wood species.
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